


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=7   ,    snake_case=3   ,    snake_case=18   ,    snake_case=30   ,    snake_case=400   ,    snake_case=True   ,    snake_case=None   ,    snake_case=True   ,    snake_case=None   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							size if size is not None else {"shortest_edge": 20}
						UpperCamelCase__       =							crop_size if crop_size is not None else {"height": 18, "width": 18}
						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							num_channels
						UpperCamelCase__       =							image_size
						UpperCamelCase__       =							min_resolution
						UpperCamelCase__       =							max_resolution
						UpperCamelCase__       =							do_resize
						UpperCamelCase__       =							size
						UpperCamelCase__       =							do_center_crop
						UpperCamelCase__       =							crop_size







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       str 			=		MobileNetVaImageProcessor if is_vision_available() else None




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							MobileNetVaImageProcessingTester(self  )




			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(snake_case   ,    "do_resize"  )  )
						self.assertTrue(hasattr(snake_case   ,    "size"  )  )
						self.assertTrue(hasattr(snake_case   ,    "do_center_crop"  )  )
						self.assertTrue(hasattr(snake_case   ,    "crop_size"  )  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size   ,    {"shortest_edge": 20}  )
						self.assertEqual(image_processor.crop_size   ,    {"height": 18, "width": 18}  )

						UpperCamelCase__       =							self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42   ,    crop_size=84  )
						self.assertEqual(image_processor.size   ,    {"shortest_edge": 42}  )
						self.assertEqual(image_processor.crop_size   ,    {"height": 84, "width": 84}  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						pass




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						UpperCamelCase__       =							prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case  )
						for image in image_inputs:
									self.assertIsInstance(snake_case   ,    Image.Image  )

						# Test not batched input
						UpperCamelCase__       =							image_processing(image_inputs[0]   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,    )

						# Test batched
						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,    )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						UpperCamelCase__       =							prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case   ,    numpify=snake_case  )
						for image in image_inputs:
									self.assertIsInstance(snake_case   ,    np.ndarray  )

						# Test not batched input
						UpperCamelCase__       =							image_processing(image_inputs[0]   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,    )

						# Test batched
						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,    )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						UpperCamelCase__       =							prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case   ,    torchify=snake_case  )
						for image in image_inputs:
									self.assertIsInstance(snake_case   ,    torch.Tensor  )

						# Test not batched input
						UpperCamelCase__       =							image_processing(image_inputs[0]   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,    )

						# Test batched
						UpperCamelCase__       =							image_processing(snake_case   ,    return_tensors="pt"  ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )   ,    )






from __future__ import annotations

import numpy as np
def 							UpperCamelCase_(					_A :list[float]	)->		Optional[Any]:
			return np.maximum(0					,  _A	)


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


